One of the greatest infrastructure challenges in organizations today is the reliance on database systems created and maintained over a period of time much longer than their anticipated lifespan. Many of these systems were created with numerous limitations and restrictions due to technological restraints of the time period. Over time, technology has rapidly improved and many of these systems have become outdated and inefficient. As a result, many organizations are looking for a viable approach to modernize their legacy database systems.
Past attempts at legacy database modernization have generally included direct software updates and/or data conversions. A first approach to legacy database modernization involves creating a new data store and uploading an entire legacy database into the new store in a single modernization attempt. One problem with this approach is that undetected flaws in the modernization software may result in unacceptable amounts of lost and/or destroyed data.
Another approach to legacy database modernization involves performing a record by record conversion of legacy source data into a new data store format. Although the occurrence of lost and/or destroyed data may be reduced, this approach may be both time-consuming and cost-prohibitive.
Furthermore, the ability to identify inefficiencies within today's data modernization systems is limited. Typically, the ability of a user of a data modernization system is limited to a flat-formatted file that reports aggregate success-failure rates for an entire data transformation/migration. Thus, a user of current data modernization system does not have the ability to pinpoint specific trouble spots within a data modernization system.